Application of machine learning techniques to simulate the evaporative fraction and its relationship with environmental variables in corn crops
Tóm tắt
The evaporative fraction (EF) represents an important biophysical parameter reflecting the distribution of surface available energy. In this study, we investigated the daily and seasonal patterns of EF in a multi-year corn cultivation located in southern Italy and evaluated the performance of five machine learning (ML) classes of algorithms: the linear regression (LR), regression tree (RT), support vector machine (SVM), ensembles of tree (ETs) and Gaussian process regression (GPR) to predict the EF at daily time step. The adopted methodology consisted of three main steps that include: (i) selection of the EF predictors; (ii) comparison of the different classes of ML; (iii) application, cross-validation of the selected ML algorithms and comparison with the observed data. Our results indicate that SVM and GPR were the best classes of ML at predicting the EF, with a total of four different algorithms: cubic SVM, medium Gaussian SVM, the Matern 5/2 GPR, and the rational quadratic GPR. The comparison between observed and predicted EF in all four algorithms, during the training phase, were within the 95% confidence interval: the R2 value between observed and predicted EF was 0.76 (RMSE 0.05) for the medium Gaussian SVM, 0.99 (RMSE 0.01) for the rational quadratic GPR, 0.94 (RMSE 0.02) for the Matern 5/2 GPR, and 0.83 (RMSE 0.05) for the cubic SVM algorithms. Similar results were obtained during the testing phase. The results of the cross-validation analysis indicate that the R2 values obtained between all iterations for each of the four adopted ML algorithms were basically constant, confirming the ability of ML as a tool to predict EF. ML algorithms represent a valid alternative able to predict the EF especially when remote sensing data are not available, or the sky conditions are not suitable. The application to different geographical areas, or crops, requires further development of the model based on different data sources of soils, climate, and cropping systems.
Tài liệu tham khảo
citation_journal_title=Remote Sens; citation_title=Integrated methodology for estimating water use in mediterranean agricultural areas; citation_author=T Alexandridis, I Cherif, Y Chemin, G Silleos, E Stavrinos, G Zalidis; citation_volume=1; citation_publication_date=2009; citation_pages=445-465; citation_doi=10.3390/rs1030445; citation_id=CR1
citation_journal_title=J Irrig Drain Eng ASCE; citation_title=Satellite-based energy balance for mapping evapotranspiration with internalized calibration (METRIC)-Model; citation_author=RG Allen, M Tasumi, R Trezza; citation_volume=133; citation_publication_date=2007; citation_pages=380-394; citation_doi=10.1061/(ASCE)0733-9437(2007)133:4(380); citation_id=CR2
citation_journal_title=J Geophys Res-Atmos; citation_title=A climatological study of evapotranspiration and moisture stress across the continental United States based on the thermal remote sensing: 1. Model formulation; citation_author=MC Anderson, JM Norman, JR Mecikalski, JA Otkin, WP Kustas; citation_volume=112; citation_publication_date=2007; citation_pages=D10117; citation_doi=10.1029/2006JD007506; citation_id=CR3
citation_journal_title=Eng Appl Comput Fluid Mech; citation_title=Daily pan-evaporation estimation in different agro-climatic zones using novel hybrid support vector regression optimized by Salp swarm algorithm in conjunction with gamma test; citation_author=M Anurag, T Yazid, AA Nadhir, ShH Shamsuddin, S Harkanwaljot, RKP Sekhon, KP Priya Rai, S Padam, E Ahmed, S Saad; citation_volume=15; citation_issue=1; citation_publication_date=2021; citation_pages=1075-1094; citation_id=CR4
citation_journal_title=Sol Energy; citation_title=A novel grouping genetic algorithm-extreme learning machine approach for global solar radiation prediction from numerical weather models inputs; citation_author=A Aybar-Ruiz, S Jiménez-Fernández, L Cornejo-Bueno, C Casanova-Mateo, J Sanz-Justo, P Salvador-González, S Salcedo-Sanz; citation_volume=132; citation_publication_date=2016; citation_pages=129-142; citation_doi=10.1016/j.solener.2016.03.015; citation_id=CR5
citation_journal_title=J Geophys Res Atmos; citation_title=The influence of land cover on surface energy partitioning and evaporative fraction regimes in the US Southern Great Plains; citation_author=JE Bagley, LM Kueppers, DP Billesbach, IN Williams, SC Biraud, MS Torn; citation_volume=122; citation_issue=11; citation_publication_date=2017; citation_pages=5793-5807; citation_doi=10.1002/2017JD026740; citation_id=CR6
citation_journal_title=Glob Change Biol; citation_title=How eddy covariance flux measurements have contributed to our understanding of global change biology; citation_author=DD Baldocchi; citation_volume=26; citation_issue=1; citation_publication_date=2020; citation_pages=242-260; citation_doi=10.1111/gcb.14807; citation_id=CR7
citation_journal_title=Geoderma; citation_title=Mapping LUCAS topsoil chemical properties at European scale using Gaussian process regression; citation_author=C Ballabio, E Lugato, O Fernández-Ugalde, A Orgiazzi, A Jones, P Borrelli, P Panagos; citation_volume=355; citation_publication_date=2019; citation_doi=10.1016/j.geoderma.2019.113912; citation_id=CR8
citation_journal_title=Expert Syst Appl; citation_title=Machine learning models and bankruptcy prediction; citation_author=F Barboza, H Kimura, E Altman; citation_volume=83; citation_publication_date=2017; citation_pages=405-417; citation_doi=10.1016/j.eswa.2017.04.006; citation_id=CR9
citation_journal_title=Formul J Hydrol; citation_title=A remote sensing surface energy balance algorithm for land (SEBAL)—1; citation_author=WGM Bastiaanssen, M Menenti, RA Feddes, AAM Holtslag; citation_volume=213; citation_publication_date=1998; citation_pages=198-212; citation_doi=10.1016/S0022-1694(98)00253-4; citation_id=CR10
citation_journal_title=J Hydrol; citation_title=Variational assimilation of land surface temperature and the estimation of surface energy balance components; citation_author=SM Bateni, D Entekhabi, DS Jeng; citation_volume=481; citation_publication_date=2013; citation_pages=143-156; citation_doi=10.1016/j.jhydrol.2012.12.039; citation_id=CR11
citation_journal_title=Agric Water Manag; citation_title=New approach to estimate daily reference evapotranspiration based on hourly temperature and relative humidity using machine learning and deep learning; citation_author=L Borges, F Fernando, F Cunha; citation_volume=234; citation_publication_date=2020; citation_doi=10.1016/j.agwat.2020.106113; citation_id=CR12
citation_journal_title=Mach Learn; citation_title=Random forests; citation_author=L Breiman; citation_volume=45; citation_issue=1; citation_publication_date=2001; citation_pages=5-32; citation_doi=10.1023/A:1010933404324; citation_id=CR13
citation_journal_title=Int J Remote Sens; citation_title=A new method for estimating of evapotranspiration and surface soil moisture from optical and thermal infrared measurements: the simplified triangle; citation_author=TN Carlson, GP Petropoulos; citation_volume=40; citation_issue=20; citation_publication_date=2019; citation_pages=7716-7729; citation_doi=10.1080/01431161.2019.1601288; citation_id=CR14
Dash SS, Nayak SK, Mishra D (2021) A review on machine learning algorithms. Intell Cloud Comput 495–507.
citation_journal_title=Rem Sens Environ; citation_title=Validation and scale dependencies of the triangle method for the evaporative fraction estimation over heterogeneous areas; citation_author=A Tomás, H Nieto, R Guzinski, J Salas, I Sandholt, P Berliner; citation_volume=152; citation_publication_date=2014; citation_pages=493-511; citation_doi=10.1016/j.rse.2014.06.028; citation_id=CR16
citation_journal_title=Comput Electron Agricult; citation_title=Evapotranspiration estimation using four different machine learning approaches in different terrestrial ecosystems; citation_author=X Dou, Y Yang; citation_volume=148; citation_publication_date=2018; citation_pages=95-106; citation_doi=10.1016/j.compag.2018.03.010; citation_id=CR17
citation_journal_title=Bound-Layer Meteorol; citation_title=Cospectral correction model for measurement of turbulent NO2 flux; citation_author=W Eugster, WA Senn; citation_volume=74; citation_issue=4; citation_publication_date=1995; citation_pages=321-340; citation_doi=10.1007/BF00712375; citation_id=CR18
citation_journal_title=Geophys Res Lett; citation_title=Prolongation of SMAP to spatiotemporally seamless coverage of continental U.S. using a deep learning neural network; citation_author=K Fang, C Shen, D Kifer, X Yang; citation_volume=44; citation_publication_date=2017; citation_pages=11030-11039; citation_doi=10.1002/2017GL075619; citation_id=CR19
citation_journal_title=Ecol Appl; citation_title=The energy balance closure problem—an overview; citation_author=T Foken; citation_volume=18; citation_publication_date=2008; citation_pages=1351-1367; citation_doi=10.1890/06-0922.1; citation_id=CR20
citation_journal_title=J Hydrol; citation_title=A novel integrated method based on a machine learning model for estimating evapotranspiration in dryland; citation_author=T Fu, X Li, R Jia, L Feng; citation_volume=603; citation_publication_date=2021; citation_doi=10.1016/j.jhydrol.2021.126881; citation_id=CR21
citation_journal_title=Agric for Meteorol; citation_title=Analysis of evaporative fraction diurnal behaviour; citation_author=P Gentine, D Entekhabi, A Chehbouni, G Boulet, B Duchemin; citation_volume=143; citation_issue=1–2; citation_publication_date=2007; citation_pages=13-29; citation_doi=10.1016/j.agrformet.2006.11.002; citation_id=CR22
citation_journal_title=J Hydrometeorol; citation_title=The diurnal behavior of evaporative fraction in the soil–vegetation–atmospheric boundary layer continuum; citation_author=P Gentine, D Entekhabi, J Polcher; citation_volume=12; citation_issue=6; citation_publication_date=2011; citation_pages=1530-1546; citation_doi=10.1175/2011JHM1261.1; citation_id=CR23
citation_journal_title=Agric for Meteorol; citation_title=A combination of quality assessment tools for eddy covariance measurements with footprint modelling for the characterization of complex sites; citation_author=M Göckede, C Rebmann, T Foken; citation_volume=127; citation_publication_date=2004; citation_pages=175-188; citation_doi=10.1016/j.agrformet.2004.07.012; citation_id=CR24
Guevara-Escobar A, González-Sosa E, Cervantes-Jiménez M, Suzán-Azpiri H, Queijeiro-Bolaños ME, Carrillo Ángeles I, Cambrón-Sandoval VH (2020) Eddy covariance carbon flux in a scrub in the Mexican highland.
Biogeosci Discuss 2020:1-16
citation_journal_title=Tree Physiol; citation_title=Uncertainty in eddy covariance measurements and its application to physiological models; citation_author=DY Hollinger, AD Richardson; citation_volume=25; citation_publication_date=2005; citation_pages=873-885; citation_doi=10.1093/treephys/25.7.873; citation_id=CR26
citation_journal_title=Bound Layer Meteorol; citation_title=Attenuation of scalar fluxes measured with spatially displaced sensors; citation_author=TW Horst, DH Lenschow; citation_volume=130; citation_publication_date=2009; citation_pages=275-300; citation_doi=10.1007/s10546-008-9348-0; citation_id=CR27
citation_journal_title=Agric for Meteorol; citation_title=Optical-based and thermal-based surface conductance and actual evapotranspiration estimation, an evaluation study in the North China Plain; citation_author=X Hu, L Shi, L Lin, B Zhang, Y Zha; citation_volume=263; citation_publication_date=2018; citation_pages=449-464; citation_doi=10.1016/j.agrformet.2018.09.015; citation_id=CR28
citation_journal_title=Agric for Meteorol; citation_title=Nonlinear boundaries of land surface temperature–vegetation index space to estimate water deficit index and evaporation fraction; citation_author=X Hu, L Shi, L Lin, Y Zha; citation_volume=279; citation_publication_date=2019; citation_doi=10.1016/j.agrformet.2019.107736; citation_id=CR29
citation_journal_title=Computers; citation_title=Improved measures of redundancy and relevance for mRMR feature selection; citation_author=I Jo, S Lee, S Oh; citation_volume=8; citation_issue=2; citation_publication_date=2019; citation_pages=42; citation_doi=10.3390/computers8020042; citation_id=CR30
citation_journal_title=Int J Radiat Oncol Biol Phys; citation_title=Machine learning approaches for predicting radiation therapy outcomes: a clinician’s perspective; citation_author=J Kang, R Schwartz, J Flickinger, S Beriwal; citation_volume=93; citation_publication_date=2015; citation_pages=1127-1135; citation_doi=10.1016/j.ijrobp.2015.07.2286; citation_id=CR31
citation_journal_title=Tech Rep Max Planck Inst Biogeochem; citation_title=Eddysoft-documentation of a software package to acquire and process Eddy covariance data; citation_author=O Kolle, C Rebmann; citation_volume=10; citation_publication_date=2007; citation_pages=88; citation_id=CR32
citation_journal_title=Nucleic Acids Res; citation_title=Assess the protein-coding potential of transcripts using sequence features and support vector machine; citation_author=L Kong, Y Zhang, ZQ Ye, XQ Liu, SQ Zhao, L Wei, G Gao; citation_volume=35; citation_publication_date=2007; citation_pages=345-349; citation_doi=10.1093/nar/gkm391; citation_id=CR33
citation_journal_title=Hydrol Earth Syst Sci; citation_title=Examination of evaporative fraction diurnal behaviour using a soil-vegetation model coupled with a mixed-layer model; citation_author=JP Lhomme, E Elguero; citation_volume=3; citation_publication_date=1999; citation_pages=259-270; citation_doi=10.5194/hess-3-259-1999; citation_id=CR34
citation_journal_title=Sensors; citation_title=Machine learning in agriculture: A review; citation_author=KG Liakos, P Busato, D Moshou, S Pearson, D Bochtis; citation_volume=18; citation_issue=8; citation_publication_date=2018; citation_pages=2674; citation_doi=10.3390/s18082674; citation_id=CR35
citation_journal_title=Remote Sens Environ; citation_title=The microwave temperature vegetation drought index (MTVDI) based on AMSR-E brightness temperatures for long-term drought assessment across China (2003–2010); citation_author=L Liu, J Liao, X Chen, G Zhou, Y Su, Z Xiang, Z Wang, X Liu, Y Li, J Wu, X Xiong, H Shao; citation_volume=199; citation_publication_date=2017; citation_pages=302-320; citation_doi=10.1016/j.rse.2017.07.012; citation_id=CR36
citation_journal_title=J Hydrol; citation_title=Diagnosing environmental controls on actual evapotranspiration and evaporative fraction in a water-limited region from northwest China; citation_author=Q Liu, T Wang, Q Han, S Sun, CQ Liu, X Chen; citation_volume=578; citation_publication_date=2019; citation_doi=10.1016/j.jhydrol.2019.124045; citation_id=CR37
citation_journal_title=J Hydrol; citation_title=Evaporative fraction and its application in estimating daily evapotranspiration of water-saving irrigated rice field; citation_author=X Liu, J Xu, X Zhou, W Wang, S Yang; citation_volume=584; citation_publication_date=2020; citation_doi=10.1016/j.jhydrol.2019.124317; citation_id=CR38
citation_journal_title=Aquaculture; citation_title=Fast detection of pathogens in salmon farming industry; citation_author=XA López-Cortés, FM Nachtigall, VR Olate, M Araya, S Oyanedel, V Diaz, E Jakob, M Ríos-Momberg, LS Santos; citation_volume=470; citation_publication_date=2017; citation_pages=17-24; citation_doi=10.1016/j.aquaculture.2016.12.008; citation_id=CR39
citation_journal_title=Hydrol Process; citation_title=Evaluating the SEBS-estimated evaporative fraction from MODIS data for a complex underlying surface; citation_author=J Lu, ZL Li, R Tang, BH Tang, H Wu, F Yang, G Zhou; citation_volume=27; citation_issue=22; citation_publication_date=2013; citation_pages=3139-3149; citation_id=CR40
citation_journal_title=Remote Sens; citation_title=Derivation of daily evaporative fraction based on temporal variations in surface temperature, air temperature, and net radiation; citation_author=J Lu, R Tang, H Tang, ZL Li; citation_volume=5; citation_issue=10; citation_publication_date=2013; citation_pages=5369-5396; citation_doi=10.3390/rs5105369; citation_id=CR41
citation_journal_title=Water; citation_title=Actual evapotranspiration estimates in arid cold regions using machine learning algorithms with in situ and remote sensing data; citation_author=J Mosre, F Suárez; citation_volume=13; citation_issue=6; citation_publication_date=2021; citation_pages=870; citation_doi=10.3390/w13060870; citation_id=CR42
citation_journal_title=Hydrol Sci J; citation_title=Current state of Mediterranean water resources and future trends under climatic and anthropogenic changes; citation_author=M Milano, D Ruelland, S Fernandez, A Dezetter, J Fabre, E Servat, JM Fritsch, S Ardoin-Bardin, G Thivet; citation_volume=58; citation_publication_date=2013; citation_pages=498-518; citation_doi=10.1080/02626667.2013.774458; citation_id=CR43
Moncrieff JB, Clement R, Finnigan J, Meyers T (2004) Averaging, detrending and filtering of eddy covariance time series. In: Lee X, Massman WJ, Law BE (eds) Handbook of micrometeorology: a guide for surface flux measurement and analysis. Kluwer Academic Publisher, Dordrecht, pp 7–32
citation_journal_title=Remote Sens Environ; citation_title=Development of a global evapotranspiration algorithm based on MODIS and global meteorology data; citation_author=Q Mu, F Heinsch, M Zhao, S Running; citation_volume=111; citation_publication_date=2007; citation_pages=519-536; citation_doi=10.1016/j.rse.2007.04.015; citation_id=CR45
citation_journal_title=Water; citation_title=Perceptions of present and future climate change impacts on water availability for agricultural systems in the western Mediterranean region; citation_author=TPL Nguyen, L Mula, R Cortignani, G Seddaiu, G Dono, SG Virdis, PP Roggero; citation_volume=8; citation_issue=11; citation_publication_date=2016; citation_pages=523; citation_doi=10.3390/w8110523; citation_id=CR46
citation_journal_title=J Geophys Res-Atmos; citation_title=An operational remote sensing algorithm of land surface evaporation; citation_author=K Nishida, RR Nemani, SW Running, JM Glassy; citation_volume=108; citation_issue=D9; citation_publication_date=2003; citation_pages=4270; citation_doi=10.1029/2002JD002062; citation_id=CR47
citation_journal_title=Agric for Meteorol; citation_title=A two-source approach for estimating soil and vegetation energy fluxes from observations of directional radiometric surface temperature; citation_author=JM Norman, WP Kustas, KS Humes; citation_volume=77; citation_publication_date=1995; citation_pages=263-293; citation_doi=10.1016/0168-1923(95)02265-Y; citation_id=CR48
citation_journal_title=Remote Sens; citation_title=Evaporative fraction as an indicator of moisture condition and water stress status in semi-arid rangeland ecosystems; citation_author=F Nutini, M Boschetti, G Candiani, S Bocchi, PA Brivio; citation_volume=6; citation_issue=7; citation_publication_date=2014; citation_pages=6300-6323; citation_doi=10.3390/rs6076300; citation_id=CR49
Op de Beeck M, Sabbatini S, Papale D (2017) ICOS ecosystem instructions for soil meteorological measurements (TS, SWC, G) (Version 20180615). ICOS Ecosystem Thematic Centre.
https://doi.org/10.18160/1a28-gex6
citation_journal_title=Hydrol Earth Syst Sci; citation_title=Evaluation of global terrestrial evapotranspiration using state-of-the-art approaches in remote sensing, machine learning and land surface modeling; citation_author=S Pan, N Pan, H Tian, P Friedlingstein, S Sitch, H Shi, SW Running; citation_volume=24; citation_issue=3; citation_publication_date=2020; citation_pages=1485-1509; citation_doi=10.5194/hess-24-1485-2020; citation_id=CR51
citation_journal_title=Sci Data; citation_title=The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data; citation_author=G Pastorello, C Trotta, E Canfora; citation_volume=7; citation_publication_date=2020; citation_pages=225; citation_doi=10.1038/s41597-020-0534-3; citation_id=CR52
citation_journal_title=Remote Sens; citation_title=Evaluation of daytime evaporative fraction from MODIS TOA radiances using FLUXNET observations; citation_author=J Peng, A Loew; citation_volume=6; citation_issue=7; citation_publication_date=2014; citation_pages=5959-5975; citation_doi=10.3390/rs6075959; citation_id=CR53
citation_journal_title=Hydrol Earth Syst Sci; citation_title=How representative are instantaneous evaporative fraction measurements for daytime fluxes?; citation_author=J Peng, M Borsche, Y Liu, A Loew; citation_volume=17; citation_publication_date=2013; citation_pages=3913-3919; citation_doi=10.5194/hess-17-3913-2013; citation_id=CR54
citation_journal_title=J Geophys Res Biogeosci; citation_title=Phenological versus meteorological controls on land-atmosphere water and carbon fluxes; citation_author=MJ Puma, RD Koster, BI Cook; citation_volume=118; citation_publication_date=2013; citation_pages=14-29; citation_doi=10.1029/2012JG002088; citation_id=CR55
citation_journal_title=ISPRS J Photogramm Remote Sens; citation_title=Comparative evaluation of the Vegetation Dryness Index (DVI), the Temperature Vegetation Dryness Index (TVDI) and the improved TVDI (iTVDI) for water stress detection in semi-arid regions of Iran; citation_author=P Rahimzadeh-Bajgiran, K Omasa, Y Shimizu; citation_volume=68; citation_publication_date=2012; citation_pages=1-12; citation_doi=10.1016/j.isprsjprs.2011.10.009; citation_id=CR56
citation_journal_title=Nature; citation_title=Deep learning and process understanding for data-driven Earth system science; citation_author=M Reichstein, G Camps-Valls, B Stevens, M Jung, J Denzler, N Carvalhais; citation_volume=566; citation_issue=7743; citation_publication_date=2019; citation_pages=195-204; citation_doi=10.1038/s41586-019-0912-1; citation_id=CR57
citation_journal_title=Glob change Biol; citation_title=On the separation of net ecosystem exchange into assimilation and ecosystem respiration: review and improved algorithm; citation_author=M Reichstein, E Falge, D Baldocchi, D Papale, M Aubinet, P Berbigier, R Valentini; citation_volume=11; citation_issue=9; citation_publication_date=2005; citation_pages=1424-1439; citation_doi=10.1111/j.1365-2486.2005.001002.x; citation_id=CR58
citation_journal_title=Agric for Meteorol; citation_title=A multi-site analysis of random error in tower-based measurements of carbon and energy fluxes; citation_author=AD Richardson, DY Hollinger, GG Burba; citation_volume=136; citation_publication_date=2006; citation_pages=1-18; citation_doi=10.1016/j.agrformet.2006.01.007; citation_id=CR59
citation_journal_title=Clin Biochem; citation_title=Clinical chemistry in higher dimensions: machine-learning and enhanced prediction from routine clinical chemistry data; citation_author=A Richardson, BM Signor, BA Lidbury, T Badrick; citation_volume=49; citation_publication_date=2016; citation_pages=1213-1220; citation_doi=10.1016/j.clinbiochem.2016.07.013; citation_id=CR60
citation_journal_title=IEEE Trans Pattern Anal Mach Intell; citation_title=Sensitivity analysis of k-fold cross validation in prediction error estimation; citation_author=JD Rodríguez, A Pérez, JA Lozano; citation_volume=32; citation_publication_date=2010; citation_pages=569-575; citation_doi=10.1109/TPAMI.2009.187; citation_id=CR61
citation_journal_title=Bound-Layer Meteorol; citation_title=Footprint prediction of scalar fluxes from analytical solutions of the diffusion equation; citation_author=PH Schuepp, MY Leclerc, JI MacPherson, RL Desjardins; citation_volume=50; citation_issue=1; citation_publication_date=1990; citation_pages=355-373; citation_doi=10.1007/BF00120530; citation_id=CR62
citation_journal_title=Glob Change Biol; citation_title=Assimilation exceeds respiration sensitivity to drought: a FLUXNET synthesis; citation_author=CR Schwalm, CA Williams, K Schaefer, A Arneth, D Bonal, N Buchmann, M Reichstein; citation_volume=16; citation_issue=2; citation_publication_date=2010; citation_pages=657-670; citation_doi=10.1111/j.1365-2486.2009.01991.x; citation_id=CR63
Sen PC, Hajra M, Ghosh M (2020) Supervised classification algorithms in machine learning: a survey and review. In: Mandal J, Bhattacharya D (eds) Emerging technology in modelling and graphics. Advances in Intelligent Systems and Computing, vol 937. Springer, Singapore
citation_journal_title=Nature; citation_title=Land–atmosphere coupling and climate change in Europe; citation_author=SI Seneviratne, D Luthi, M Litschi, C Schar; citation_volume=443; citation_issue=7108; citation_publication_date=2006; citation_pages=205-209; citation_doi=10.1038/nature05095; citation_id=CR65
Stein ML (1999) Interpolation of spatial data: some theory for kriging. Springer Science & Business Media.
citation_journal_title=Hydrol Earth Syst Sc; citation_title=The surface energy balance system (SEBS) for estimation of turbulent heat fluxes; citation_author=Z Su; citation_volume=6; citation_publication_date=2002; citation_pages=85-99; citation_doi=10.5194/hess-6-85-2002; citation_id=CR67
citation_journal_title=Rob Auton Syst; citation_title=Tool-body assimilation model considering grasping motion through deep learning; citation_author=K Takahashi, K Kim, T Ogata, S Sugano; citation_volume=91; citation_publication_date=2017; citation_pages=115-127; citation_doi=10.1016/j.robot.2017.01.002; citation_id=CR68
citation_journal_title=Geophys Res Lett; citation_title=An improved constant evaporative fraction method for estimating daily evapotranspiration from remotely sensed instantaneous observations; citation_author=R Tang, ZL Li; citation_volume=44; citation_publication_date=2017; citation_pages=2319-2326; citation_doi=10.1002/2017GL072621; citation_id=CR69
citation_journal_title=J Geophys Res Atmos; citation_title=Estimating daily evapotranspiration from remotely sensed instantaneous observations with simplified derivations of a theoretical model; citation_author=R Tang, Z-L Li; citation_volume=122; citation_publication_date=2017; citation_pages=10177-10190; citation_doi=10.1002/2017JD027094; citation_id=CR70
citation_journal_title=Biogeosciences; citation_title=Predicting carbon dioxide and energy fluxes across global FLUXNET sites with regression algorithms; citation_author=G Tramontana, M Jung, CR Schwalm, K Ichii, G Camps-Valls, B Ráduly, D Papale; citation_volume=13; citation_issue=14; citation_publication_date=2016; citation_pages=4291-4313; citation_doi=10.5194/bg-13-4291-2016; citation_id=CR71
citation_journal_title=J Clim; citation_title=Physical processes involved in the 1988 drought and 1993 floods in North America; citation_author=KE Trenberth, CJ Guillemot; citation_volume=9; citation_publication_date=1996; citation_pages=1288-1298; citation_doi=10.1175/1520-0442(1996)009<1288:PPIITD>2.0.CO;2; citation_id=CR72
citation_title=The nature of statistical learning theory; citation_publication_date=1999; citation_id=CR73; citation_author=V Vapnik; citation_publisher=Springer Science & Business Media
citation_journal_title=Acta Physiol Plant; citation_title=Effects of water stress on gas exchange of field grown Zea mays L. in Southern Italy: an analysis at canopy and leaf level; citation_author=L Vitale, P Tommasi, C Arena, A Fierro, AV Santo, V Magliulo; citation_volume=29; citation_issue=4; citation_publication_date=2007; citation_pages=317-326; citation_doi=10.1007/s11738-007-0041-6; citation_id=CR74
citation_journal_title=Int J Biometeorol; citation_title=The response of ecosystem carbon fluxes to LAI and environmental drivers in a maize crop grown in two contrasting seasons; citation_author=L Vitale, P Tommasi, G D’Urso, V Magliulo; citation_volume=60; citation_issue=3; citation_publication_date=2016; citation_pages=411-420; citation_doi=10.1007/s00484-015-1038-2; citation_id=CR75
citation_journal_title=IEEE Trans Neural Netw Learn Syst; citation_title=Bayesian neighborhood component analysis; citation_author=D Wang, X Tan; citation_volume=29; citation_issue=7; citation_publication_date=2017; citation_pages=3140-3151; citation_doi=10.1109/TNNLS.2017.2712823; citation_id=CR76
citation_journal_title=Autom Constr; citation_title=Support vector machine regression for project control forecasting; citation_author=M Wauters, M Vanhoucke; citation_volume=47; citation_publication_date=2014; citation_pages=92-106; citation_doi=10.1016/j.autcon.2014.07.014; citation_id=CR77
citation_journal_title=Q J R Meteorol Soc; citation_title=Correction of flux measurements for density effects due to heat and water vapour transfer; citation_author=EK Webb, G Pearman, R Leuning; citation_volume=106; citation_publication_date=1980; citation_pages=85-100; citation_doi=10.1002/qj.49710644707; citation_id=CR78
citation_journal_title=Cell Syst; citation_title=Prediction of synergism from chemical–genetic interactions by machine learning; citation_author=J Wildenhain, M Spitzer, S Dolma, N Jarvik, R White, M Roy, E Griffiths, DS Bellows, GD Wright, M Tyers; citation_volume=1; citation_publication_date=2015; citation_pages=383-395; citation_doi=10.1016/j.cels.2015.12.003; citation_id=CR79
citation_journal_title=Geophys Res Lett; citation_title=Vegetation controls on surface heat flux partitioning, and land–atmosphere coupling; citation_author=IN Williams, MS Torn; citation_volume=42; citation_issue=21; citation_publication_date=2015; citation_pages=9416-9424; citation_doi=10.1002/2015GL066305; citation_id=CR80
citation_journal_title=J Geophys Res-Atmos; citation_title=Estimation of surface turbulent heat fluxes via variational assimilation of sequences of land surface temperatures from geostationary operational environmental satellites; citation_author=T Xu, SM Bateni, S Liang, D Entekhabi, K Mao; citation_volume=119; citation_issue=18; citation_publication_date=2014; citation_pages=10780-10798; citation_id=CR81
citation_journal_title=J Integr Agric; citation_title=Analysis of the diurnal pattern of evaporative fraction and its controlling factors over croplands in the Northern China; citation_author=D Yang, W He, HE Chen, HM Lei; citation_volume=12; citation_issue=8; citation_publication_date=2013; citation_pages=1316-1329; citation_doi=10.1016/S2095-3119(13)60540-7; citation_id=CR82
citation_journal_title=Water Resour Res; citation_title=Comparison of three dual-source remote sensing evapotranspiration models during the MUSOEXE- 12 campaign: revisit of model physics; citation_author=Y Yang, D Long, H Guan, W Liang, C Simmons, O Batelaan; citation_volume=51; citation_publication_date=2015; citation_pages=3145-3165; citation_doi=10.1002/2014WR015619; citation_id=CR83
citation_journal_title=J Hydrol; citation_title=Improving terrestrial evapotranspiration estimation across China during 2000–2018 with machine learning methods; citation_author=L Yin, F Tao, Y Chen, F Liu, J Hu; citation_volume=600; citation_publication_date=2021; citation_doi=10.1016/j.jhydrol.2021.126538; citation_id=CR84
citation_journal_title=Agric for Meteorol; citation_title=Biophysical drivers of the carbon dioxide, water vapor, and energy exchanges of a short-rotation poplar coppice; citation_author=T Zenone, M Fischer, N Arriga, LS Broeckx, MS Verlinden, S Vanbeveren, D Zona, R Ceulemans; citation_volume=209; citation_publication_date=2015; citation_pages=22-35; citation_doi=10.1016/j.agrformet.2015.04.009; citation_id=CR85
citation_journal_title=Geophys Res Lett; citation_title=Physics-constrained machine learning of evapotranspiration; citation_author=WL Zhao, P Gentine, M Reichstein, Y Zhang, S Zhou, Y Wen, GY Qiu; citation_volume=46; citation_issue=24; citation_publication_date=2019; citation_pages=14496-14507; citation_doi=10.1029/2019GL085291; citation_id=CR86
citation_journal_title=J Appl Meteorol Climatol; citation_title=Biological and environmental controls on evaporative fractions at AmeriFlux sites; citation_author=C Zhou, K Wang; citation_volume=55; citation_issue=1; citation_publication_date=2016; citation_pages=145-161; citation_doi=10.1175/JAMC-D-15-0126.1; citation_id=CR87
citation_journal_title=Remote Sens Environ; citation_title=An observation-driven optimization method for continuous estimation of evaporative fraction over large heterogeneous areas; citation_author=W Zhu, S Jia, U Lall, Y Cheng, P Gentine; citation_volume=247; citation_publication_date=2020; citation_doi=10.1016/j.rse.2020.111887; citation_id=CR88
Zveryaev II, Allan RP (2010) Summertime precipitation variability over Europe and its links to atmospheric dynamics and evaporation. J Geophys Res: Atmos 115(D12).